This course studies quantitative methods used in managerial decision-making. These techniques are often referred to as management science, operations research, or decision science. Topics include mathematical modeling (linear, integer, and network programming), graphical solution techniques, sensitivity analysis, decision analysis, and forecasting.
Prerequisite Courses: MATH 1525, MATH 1526 (or equivalent courses), and BIT 2405 with a grade of C- or better.
Introduction to Management Science (13th Edition) by Bernard W. Taylor III, published by Pearson. ISBN-13: 978-0-13-473066-0.
Note: The international versions and previous editions that you can find online are not appropriate for this class, since they will have different homework problems than our version.
Our Canvas home page contains a detailed schedule for the course. For each week, it provides links for lecture materials, review materials, and homework. Please look there frequently to ensure that you are keeping up with course material.
The best ways to ask questions outside of class are on Piazza and at office hours. Piazza is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Unless your Piazza post needs to be private, please make the post public, since other students may be interested in the question and answer. If you prefer, you can post anonymously. To contact the teaching staff privately, please make a private Piazza post instead of sending an email. Direct emails will be ignored. To set up an appointment outside of office hours, create a private post to the instructor including your available times for at least three forthcoming weekdays.
If you have any problems or feedback for the developers, email team@piazza.com. You can access our class page via the Piazza link in the navigation bar.
✉️ NOTE: Use your VT email to sign up.
The course grade will be determined from the following components.
| Points | Percent | |
|---|---|---|
| 10 Quizzes | 200 | 20% |
| In-Class Activities | 100 | 10% |
| Project | 200 | 20% |
| 3 Monthly Exams | 300 | 30% |
| Final Exam | 200 | 20% |
| Total | 1000 | 100% |
We will use the standard VT grading system.
| Grade | Score Range | Grade | Score Range |
|---|---|---|---|
| A | 93.00 and above | C | 73.00 – 76.99 |
| A- | 90.00 – 92.99 | C- | 70.00 – 72.99 |
| B+ | 87.00 – 89.99 | D+ | 67.00 – 69.99 |
| B | 83.00 – 86.99 | D | 63.00 – 66.99 |
| B- | 80.00 – 82.99 | D- | 60.00 – 62.99 |
| C+ | 77.00 – 79.99 | F | 59.99 and below |
📐 Grading might be on a curve (calculated at the end of the semester), with the average course grade earning at least 800 (out of 1000). There will be no curve if the average is already above the threshold. For quizzes and exams, statistics can be accessed on Canvas to help you understand your standing in the class.
🧮 To calculate your grade, make a copy of this spreadsheet. Input your scores into the blue boxes only. For your exam 1, 2, and 3 scores, put whatever you scored – the spreadsheet will adjust the score based on your final exam grade by itself.
We will have 10 quizzes administered on Canvas outside the class throughout the semester. Quizzes will be open book, open computer, and open notes, and you are encouraged to work with your classmates. A penalty of 5% per day will be applied for late submissions.
There will be random in-class quizzes/polls. NO MAKE-UPS WILL BE OFFERED.
Students will complete a multi-stage optimization project involving formulation, extension, AI comparison, and structural modification of a scheduling model. The project emphasizes modeling rigor, constraint interaction, and critical evaluation of AI-generated solutions, with staged submissions across the semester.
We will have 3 monthly exams. Each monthly exam will count for 10% of your final course grade. These exams will consist of multiple-choice questions. For each exam, a basic calculator will be provided. If any of the exams are missed, the weight of the exam will be transferred to the final exam (see below). NO MAKE-UP EXAMS WILL BE GIVEN.
The final exam date/time/location can be found in the schedule. The final exam is cumulative, and it can only be rescheduled by the Dean (for 3 or more exams in a 24-hour period, or 2 exams at the same time). If your final exam score is better than any/all of your monthly exam scores, your final exam score will replace the monthly exam scores in the final course grade calculation. In other words, for any monthly exam i in {1, 2, 3}, the score will be updated with max {monthly_exam_score_i, final_exam_score}. For example, one got 70% in Monthly Exam 1 and 90% in Monthly Exam 2, and missed Monthly Exam 3. If she/he gets 80% in the final exam, her/his grades for the three monthly exams will be 80%, 90%, and 80%. The final exam will include multiple-choice questions.
Your feedback is essential for the continuous improvement of this course. To encourage participation, students who attend the mid-semester feedback session, facilitated by the Center for Excellence in Teaching and Learning (CETL), will earn 5 points. Additionally, if 75% of students complete the end-of-semester SPOT survey, all students will receive an extra 5 points. There is no other extra credit.
Homework problems will be assigned at the end of every topic on the last page of the slides. These exercises will not be collected and graded, but you are encouraged to solve every exercise to prepare for quizzes and exams. It may be helpful to initially work on the problems with other students, but you should eventually ensure that you can do them on your own. Answers to the homework exercises will be provided on Canvas. You are encouraged to check your solutions with the instructor or TA during office hours.
Attendance is required. If you have to miss class, you do not need to document the causes, such as illnesses and interviews. But it is your responsibility to catch up on all material, information, and assignments covered during your missed class time and appropriate arrangements will need to be made to obtain and complete all necessary coursework. If you have an illness or emergency that requires you to miss class for an extended period of time or that interferes with completing an exam, please contact the instructor to discuss the situation.
Students are encouraged to utilize AI tools to assist in understanding concepts and methods, following the guidelines from TLOS.
I expect you to behave respectfully toward your peers. Use good manners in class sessions and in office hours. Participate in class discussions, interact with your peers, stay engaged in lectures, and avoid distractions. Obey all VT health and safety guidelines.
Virginia Tech welcomes students with disabilities into the University’s educational programs. If you anticipate or experience academic barriers that may be due to disability, please contact the Services for Students with Disabilities (SSD) office (540-231-3788, ssd@vt.edu, or visit https://ssd.vt.edu/). If you have an SSD accommodation letter, please email it to me as soon as possible, so we can make the appropriate arrangements. At a minimum, you should provide 5 business days’ notice for scheduling exam accommodations.
The Undergraduate Honor Code pledge that each member of the university community agrees to abide by states: “As a Hokie, I will conduct myself with honor and integrity at all times. I will not lie, cheat, or steal, nor will I accept the actions of those who do.”
Students enrolled in this course are responsible for abiding by the Honor Code. A student who has doubts about how the Honor Code applies to any assignment is responsible for obtaining specific guidance from the course instructor before submitting the assignment for evaluation. Ignorance of the rules does not exclude any member of the University community from the requirements and expectations of the Honor Code. Academic integrity expectations are the same for online classes as they are for in-person classes. All university policies and procedures apply in any Virginia Tech academic environment. For additional information about the Honor Code, please visit https://www.honorsystem.vt.edu/.
Please plan ahead to make your semester go as smoothly as possible. Putting off lectures or studying until the last minute can make some weeks terribly stressful. Have the discipline to work ahead on your slow weeks to keep the busy weeks bearable. Always make healthy decisions – don’t stay up all night finishing a project or studying for an exam. If you ever need to make a choice between getting a good grade and taking care of yourself, you should always do what is best for your health, whether it be mental or physical. There are more important things than grades.
The course content is built upon materials made by Dr. Barbara Fraticelli and Dr. Dan Simundza.